development of a procedure to make better use of …...気象衛星センター技術報告第55号...

気象衛星センター技術報告第 55 号 2011 年 2 月

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Development of a Procedure to Make Better Use of SSM/I Data Including Navigation Errors

KUMABE Ryoji* and EGAWA Takumu**

Abstract In the course of preparing the observation data for JRA-55, some navigation errors were found in

special-sensor microwave imager (SSM/I) data obtained from Defense Meteorological Satellite Program (DMSP) satellites. We therefore developed a method of eliminating from the SSM/I TDR/SDR files any erroneous data caused by such navigation errors. Based on a very simple satellite navigation model, this method enables most of the errors to be eliminated without the need for any information other than that contained in the SSM/I data files. A description of a computer program for navigation error compensation that we developed based on this method is also provided.

1. Introduction

The special sensor microwave imager (SSM/I) installed

on Defense Meteorological Satellite Program (DMSP)

satellites is an important data source for the assimilation

of meteorological data in a long-term reanalysis project

(JRA-251) being conducted by the Japan Meteorological

Agency (JMA) that covers a 26-year period from 1979 to

2004. The JMA has just started its next reanalysis project

(JRA-55), which covers the 55-year period from 1958 to

2012. We reviewed the treatment of observation data so

as to properly assimilate as many of them into the project

as possible. SSM/I data are, of course, one of various

types of observation data that needed to be reviewed. In

the course of our review, we discovered that a small

number of SSM/I data were erroneous. The errors found

were mainly related to navigation and observation time.

We found that these could be eliminated or corrected

through the implementation of a proper quality check

(QC) procedure, and so developed a computer program

for this purpose. We are now in the process of adopting

this computer program for the JRA-55 project.

Section 2 of this paper describes the SSM/I data we

investigated, section 3 describes the problems we

identified with these data and the solutions we found to

these problems, and section 4 contains some remarks on

the characteristics and the usage of the SSM/I data.

2. SSM/I data used for the JRA-25 and JRA-55

projects

In this section, we describe the data that we used in the

earlier JRA-25 project and will be using in the ongoing

JRA-55 project. The SSM/I data used in both these

projects are basically identical. SSM/I data from DMSP-

08, DMSP-10, DMSP-11, and DMSP-13 were provided

by the National Climatic Data Center (NCDC) of the

National Oceanic and Atmospheric Administration

*Climate Prediction Division, Global Environment and Marine Department, Japan Meteorological Agency **Numerical Prediction Division, Forecast Department, Japan Meteorological Agency

（Received February 12, 2010, Accepted December 15, 2010） 1 http:// jra.kishou.go.jp/

METEOROLOGICAL SATELLITE CENTER TECHNICAL NOTE, No.55, FEBRUARY, 2011

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(NOAA) on magnetic tapes when we started the JRA-25

project. Covering the period from July 1987 to 1997, the

data provided on these tapes was in the antenna

brightness temperature data record (TDR) format.

Unfortunately, we were unable to extract a few of the data

from the tapes. We have downloaded post-1998 SSM/I

data from NOAA’s web data services, obtaining historical

data from NOAA’s Comprehensive Large Array-data

Stewardship System (CLASS2) and real-time data from

its National Weather Service Telecommunication

Gateway (NWSTG).

The quality of the SSM/I data was mainly checked by

manual observation for the JRA-25 project, and this

manual QC revealed that a few of the data had navigation

errors and were poorly calibrated. We were aware that a

few erroneous data that escaped detection in the manual

QC had been included in the project’s assimilated data,

but felt that the effect that such errors had on the

reanalyzed meteorological data could be ignored.

For the JRA-55 project, we review the SSM/I data

stored at the JMA to enable us to provide better

observation data. NCDC kindly provided us with a

complete inventory for the period from 1994 to 2008 of

the SSM/I data stored in their CLASS archive. Using this

inventory, we were able to obtain all the data that had

been unavailable to us for the JRA-25 project. CLASS’s

data cover the period from July 1994 to the present date

(as of December 2009, the range of data stored on

CLASS had been extended to cover the period up to

December 1990). As the pre-1997 SSM/I data on CLASS

are in netCDF format, the pre-1997 data held at the JMA

that were downloaded after the completion of the JRA-25

project are also in this format.

We now download SSM/I data from the NWSTG on a

daily basis. These data are then compared with the SSM/I

data inventory on CLASS every month. If our

comparison reveals that the inventory contains some data

that we had not obtained through our daily downloads, we

download the missing data from CLASS to ensure that

we have all the available data. We have been able to

acquire almost identical data as those in the CLASS

archive by following this careful acquisition process.

Inventories of SSM/I data possessed by the JMA are

provided in Excel files named “inventory_fNN.xls”

(where “NN” represents the number of the DMSP

satellite from which the data were obtained) together with

information regarding any erroneous data which we

detected in a QC.

3. Problems with and solutions to the collection of

SSM/I data

While decoding and preprocessing the SSM/I data, we

found a few problems with regard to data quality. Some

of these problems have arisen quite regularly since

satellite observations were begun. There were too many

erroneous data for the problem to be ignored or for a

manual check and correction to be performed. We

therefore developed a method of identifying navigation

errors in the data, and then created a computer program

for navigation error compensation based on this method.

We have not developed a QC algorithm for brightness

temperature, but presume that such data will be treated in

the QC process of the assimilation. The computer

program corrects erroneous data where possible and

discards them if correction is impossible. The SSM/I data

that is output by the program will be assimilated in the

JRA-55 project.

The remainder of this section describes the navigation

errors found in the SSM/I data, as well as the method

used to correct/discard such errors.

3.1 Navigation errors (1)

We identified several types of navigation errors. The

most frequently occurring of these was that the

2 http://www.class.ncdc.noaa.gov/saa/products/welcome


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positioning of some segments of the data stream seemed

odd. Fig. 1 shows the most obvious example of this type

of error. In a single file, such errors are usually found in

only a few scan lines (mostly 1 or 2 lines). However, the

number of files that contain this kind of navigation error

is quite large (this problem was encountered in more than

70% of files containing post-1995 data obtained from

DMSP-10).

Another type of navigation error that we found may

have been caused by the use of data from different

observation times. An example of this type of error is

shown in Fig. 2. This figure shows a composite of the

brightness temperatures for the SSM/I 37-GHz vertical

channel based on seven files of data obtained from

DMSP-13 on 29 December 2006. There is clearly a

discontinuity in the data obtained over the Pacific Ocean,

resulting in an unusually wide space in areas to the west

of this point. The reason for this inconsistency is that the

relevant data in

NPR.TDRR.S7.D06363.S0330.E0523.B6073031.NS

were from several days prior to the observation in

question. There are cases in which data from other

satellites are misinterpreted in this way, as well. Errors of

this kind are not common, but there were a few days for

which almost half the day’s data were erroneous.

We developed a method of detecting and eliminating

navigation errors by adopting a very simple navigation

technique. Fig. 3 shows brightness temperatures

processed by using the method. Almost all the erroneous

scan data have been eliminated. (This method does not

allow us to completely eliminate all errors, however, as

evidenced by the erroneous line that can be seen in data

for an area near the Antarctic.)

3.2 Detection of navigation errors (1)

The following is an explanation of the method used to

detect navigation errors. Because the orbit of a DMSP

satellite is almost circular, we assume the angular velocity

of a DMSP satellite to be constant. The position of the

satellite is given in latitude and longitude. Relative time

progress is measured from the time at which the satellite

Fig. 1 Brightness temperatures for the SSM/I 37-GHz vertical channel based on data obtained from DMSP-11

at 1611UTC on 25 March 1994 (NPR.TDRR.S5.D94084.S1611.E1739.A0011983.NS)


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crosses the ascending node, and the longitude is measured

from the ascending node. First, we will explain the

navigation model and estimate the satellite position from

the footprints of the scan lines contained in a TDR file.

After that, we will describe the procedures used to check

for navigation errors.

Fig. 2 Composite of the brightness temperatures for the SSM/I 37-GHz vertical channel based on data obtained

from DMSP-13 on 29 December 2006 (NPR.TDRR.S7.D06363.S0330.E0523.B6073031.NS)

Fig. 3 Same as Fig. 1 but with erroneous data eliminated


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3.2.1 Prediction of the satellite’s position from the

navigation parameters

Let us assume that we know the satellite’s inclination,

the period of its rotation around the Earth (T), the time

that has elapsed since it crossed the ascending node, and

its ascending node (the time and the longitude). Using

these parameters, we can predict the future position of the

satellite.

Let A be the distance from the ascending node to the

satellite, B the latitude, and C the relative longitude of the

satellite, where γβα ,, are the angles shown in Fig. 4.

Notice that β =π - the inclination of the satellite orbit.

The distance A at the elapsed time TΔ from the node

can be expressed as follows.

TTA Δ×= /2π . (1)

Provided that 2/πα = , spherical trigonometry yields

the following equation, where the longitude and latitude

of the satellite at TΔ are:

.cos

cossinsin

,sinsinsin

BAC

ABβ

β×

=

×= (2)

Longitude C needs to be modified to take into

consideration the period of the Earth’s rotation (Te).

Because the Earth rotates by TTe Δ×/2π in TΔ , C

should be modified as follows.

TTeCC Δ×−=′ /2π

Given this, the satellite longitude C’’ is shown as:

''' 0 CCC −= (3)

We can now estimate the position of the satellite at

TΔ , provided we know the longitude of the node 0C

and the inclination.

3.2.2 Estimation of orbital parameters from the SSM/I

data stream

Orbital parameters such as β (inclination), and the

timing and longitude of the satellite’s ascending node can

be estimated by using the footprints in a scan line from

the SSM/I data stream.

First, we estimate the satellite’s position from the

footprints. Fig. 5 shows the relationship between the

satellite’s position (P, Q, and R) and the footprints in a

scan line (A, B, C) given that the SSM/I is a conical scan

sensor. While the SSM/I sensor scans the Earth from A to

Fig. 4 Illustration of a satellite orbit


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C, the satellite moves from P to R and the shapes of the

footprints deviate slightly from being perfect semicircles.

However, we have been able to confirm that we can

approximate with sufficient precision that the footprints

make a circular arc from A, B to C with the center at Q, as

shown in the right hand side of Fig. 5 (if the direction

from P to R is constant, the two situations shown in Fig.5

lead to the same result).

The latitudes and longitudes of A, B, and C are

included in the TDR data stream, and L is a constant that

is known from the DMSP satellite specifications. By

using the spherical trigonometry shown in Appendix 1,

we can perform the following procedure.

(a) Calculate the distance between A and C and the

related angles (equations 1 and 2 in Appendix 1).

(b) Determine the latitude and longitude of M, which is

the central point between A and C (equation 3 in

Appendix 1).

(c) Determine the angle of NBQ (where N represents the

North or South Pole). We are able to determine this

because we know the latitudes and longitudes of B

and M (equation 2 in Appendix 1).

Determine the latitude and longitude of Q (the position

of the satellite). We are able to determine this because the

distance between B and Q is equal to L (equation 3 in

Appendix 1). We can also determine the direction from P

to B, which is assumed to be the direction of the velocity

of the satellite.

We now know the values for the following parameters

shown in Fig. 4: γ , B and 2/πα = . If we apply

spherical trigonometry again, we can determine the

inclinationβ :

γβ sincoscos B= . (4)

Once we know that, we can also determine the remaining

two parameters shown in Fig. 4, A and C, by means of

trigonometry, and then calculate the timing of the

ascending node by using equation (1) above.

With the exception of T (i.e. the period of the satellite’s

rotation around the Earth), all of the parameters that we

need to perform a navigation error compensation can be

estimated by using the procedure described above. We

can find out the nominal value of T (about 110 minutes)

from the specifications for DMSP satellites, but this value

is not precise enough for our purposes, and we found that

T changes over time. We can therefore obtain T from the

SSM/I data stream by monitoring the time at which the

Fig. 5 The relationship between a scan line and the movement of the satellite


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latitude of a scan line moved from the Southern

Hemisphere to the Northern Hemisphere in the

observations obtained from each orbit.

3.2.3 Correction of navigation errors (1)

The procedure for correcting navigation errors is as

follows.

1. Obtain the satellite position from a scan line of the

SSM/I data and calculate the orbital parameters from

the satellite position (see section 3.2.2).

2. Calculate the satellite position for the next scan line

by using the navigation model with the parameters

calculated in step 1 (see section 3.2.1).

3. Obtain the satellite position from the next scan line as

described in step 1 (see section 3.2.2).

4. Compare the satellite positions obtained in steps 2 and

3. If the difference between the positions is greater

than the specified criterion, the data for the second

scan line is discarded.

5. Repeat the above steps for the remaining scan lines.

When we calculate the satellite position and the orbital

parameters, we check whether the values obtained are

appropriate by comparing them with the nominal values

for the period of rotation T, the inclinationβ , and the

specified timing of the ascending node.

In step 4, criteria are empirically chosen and set for the

direction and speed of the satellite’s movement. The

precision of a satellite’s positioning is worse at higher

latitudes, and the precision with which a satellite’s

position can be predicted by using the orbital parameters

obtained based on this positioning is also worse at higher

latitudes. Therefore, the criteria set for higher latitudes

are greater than those set for lower latitudes. All scan

lines after the second one are checked according to the

above procedure, but the first scan line cannot be checked

in this way. Given this, if a QC of the second line results

in a fail, the first scan line is discarded instead of the

second one.

The type of navigation error shown in Fig. 1 can be

easily eliminated by following the above procedure, but

the type shown in Fig. 2 cannot be detected using this

procedure. The latter type of error can be detected by

using T, which is obtained from the TDR data. For the

purposes of our study, we assumed that the node is the

position located at the center of a scan data item when it

passes from the Southern Hemisphere into the Northern

Hemisphere, and so documented the longitude and time

for each orbit of the satellite. We then calculated T for

each orbit. The longitude of the node for the orbit under

observation was then compared with the longitude of the

node estimated by using the value for T obtained when

the node for the orbit was documented. If the difference

was greater than the specified criterion, the observation in

the file was discarded (in such cases, the data for the

whole day were discarded). However, the monitored T is

not precise enough to predict the ascending node beyond

a few days in advance. Consequently, in the computer

program we used, this method of performing a QC is only

efficient for up to three days.

3.3 Navigation errors (2)

An example of another kind of navigation error is

shown in Fig. 6. This figure shows brightness

temperatures in the 37-GHz vertical channel from DMSP-

08 at 2249UTC on 24 August 1987. Although the

presence of navigation errors is clearly visible in this

figure, no such errors were identified in the QC of the

data files. In microwave images, the brightness

temperatures of sea and land are quite distinct, so the

coasts of the continents are clearly visible in such images

and they should match a geographical map, as is the case

in Fig. 2, for example. However, the brightness

temperature distributions in Fig. 6 do not match the

geography, as the dark area in the brightness temperatures

around the east coast of North America should correspond


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to the South American continent. We presume that the

positions and observation times recorded in the data file

are out of phase with the brightness temperature data.

This kind of error seems to occur only with pre-1994 data

files.

The method we adopted for dealing with this kind of

navigation data error is simple. We made use of the fact

that the brightness temperature of land is much higher

than that of an ocean. We counted the number of

observation points in the ocean area for which the

brightness temperature with a vertical polarization

component of 19 GHz was higher than the brightness

temperature criterion of 253 K. (This criterion is

considered to be the highest ocean temperature).

Therefore the data in which a brightness temperature in

the oceans exceeds the criterion should be discarded.

However, we often encountered data that were corrupted

by observation noise and geographical mapping errors.

After a few trials, we concluded that data should be

discarded if 10% of the observation points had higher

brightness temperatures than the criterion, on the basis

that this meant the data were navigationally erroneous.

This method works well in most cases, the exception

being cases where the number of ocean scans was very

small or the regions scanned were at high latitude because

the land–ocean temperature contrast is small at higher

latitudes. Given this, we only performed this QC for data

obtained from observation points located lower than 70

degrees latitude. This method will have to be further

refined in the future.

3.4 Format error encountered in converting the

original SSM/I data into netCDF format

The CLASS web site began offering SSM/I data in

netCDF format in August 2008 and, at present, SSM/I

TDR data before August 1997 are only available in

netCDF format. On finding out that there were some data

in netCDF format on CLASS that we did not have, we

downloaded the data and tried to make use of them.

Fig. 6 Brightness temperatures for the SSM/I 37-GHz vertical channel obtained based on data from DMSP-08

at 2249UTC on 24 August 1987 (NPR.TDRR.N8.D87236.S2249.E0032.B0093233.FN)


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However, we soon found that there was a problem with

the data. The data appeared to be valid if the observation

times attached to each scan line were ignored, but there

were some navigation errors like those found in the

original TDR data files. It appears that the SSM/I data in

netCDF format are not identical to the original TDR file

data. Instead of data having been extracted from every

scan line, they were extracted from every few scan lines

(i.e. certain data were culled), and it appears that in the

process of doing so, correspondence between the

observation time for each scan line and the relevant data

was lost. We need the precise observation time for each

scan line in order to be able to perform the navigation QC.

All of the netCDF files failed the QC we performed on

them, so we have not yet been able to use any SSM/I data

in the netCDF format. This problem needs to be resolved

in the next reanalysis project.

According to the communication that we received from

NCDC on 30 January 2010, this problem affects only

netCDF files containing data from between early 1993

and February 1997. NCDC are working to resolve this

problem (see the CLASS home page3 for further details).

4. Conclusion

The concluding remarks of this paper are as follows.

1. SSM/I data are very useful for obtaining information

regarding the surface of the Earth and its low-level

atmosphere. The long observation period over which

these data are collected makes them a particularly

attractive option.

2. However, the data contain some errors related to

navigation and brightness temperature. Although in most

cases such errors are found in just a few lines of a TDR

data file, we find a few TDR files in which all of the data

are rendered useless by navigation errors. Appendix 3

tabulates the TDR files of this kind.

3. We developed a method of mitigating navigation

errors, and adopted it for the preparation of observation

data for the JRA-55 reanalysis project. This method can

eliminate quite a few of the navigation errors found in

SSM/I data from DMSP satellites.

This method is not perfect, however, and we will have

to refine its performance or develop a better method. The

review and reprocessing of data on CLASS might prove

useful as a part of this development.

The source code for the computer program we

developed for navigation error compensation is given in

Appendix 2. The subroutine Chk_orbit is called each time

the data in an entire scan line is read out, and the program

then reports whether any navigation data errors have been

identified. Those interested are free to use this code,

either in part or in whole, without restriction. This

computer program is intended to be applied to a time-

ordered series of data files because it needs to monitor the

ascending node of the satellites. The efficiency and

reliability of the program is therefore limited. Persons

using this program will need to monitor the performance

manually, especially at the beginning of the process and

on days for which there are a lot of erroneous data.

Reference

Kazutoshi Onogi, 2007: The JRA-25 reanalysis, JMSC,

85, 369-432.

3 As of August 2010, the problem with the netCDF files on CLASS as described in this section has been completely resolved. The

correct version of the netCDF file is VER2_5. The authors are grateful for the NCDC’s prompt response to this issue and for their

efforts to confirm and resolve the problem.


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Appendix 1: Some results obtained using spherical trigonometry

If we construct a triangle on the above globe with vertices A, B and O (where O represents the North Pole), we can

obtain the following formula.

)cos(coscossinsincos 122121 λλϕϕϕϕ −−=Θ (1)

βϕ

αϕ

θ sincos

sincos

sinsin 12 ==

Θ (2)

We can determine α and β from equation (2) if we know 11,λϕ and 22 ,λϕ , because we can determine

θsin/sinΘ from equation (1).

In addition, if we know Θ,, 11 λϕ , andα , then we can determine 2ϕ using equation (3), and because we know

θ from equation (2), we can then determine 2λ using equation (1).

αϕϕϕ cossincoscossinsin 112 Θ+Θ= (3)

development of a procedure to make better use of …...気象衛星センター技術報告 第55号...

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development of a procedure to make better use of …...気象衛星センター技術報告第55号...